| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and trends® in machine learning 14 (1–2), 1-210, 2021 | 5533 | 2021 |
| Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data P Vepakomma, O Gupta, T Swedish, R Raskar | 611 | 2019 |
| Fedml: A research library and benchmark for federated machine learning C He, S Li, J So, M Zhang, H Wang, X Wang, P Vepakomma, A Singh, ... SpicyFL, NeurIPS 2020, 2020 | 397 | 2020 |
| Detailed comparison of communication efficiency of split learning and federated learning A Singh, P Vepakomma, O Gupta, R Raskar https://arxiv.org/pdf/1909.09145.pdf, 2019 | 391* | 2019 |
| A-Wristocracy: Deep Learning on Wrist-worn Sensing for Recognition of User Complex Activities P Vepakomma, D De, SK Das, S Bhansali IEEE Body Sensor Networks Conference, 2015 | 171 | 2015 |
| Privacy in Deep Learning: A Survey F Mirshghallah, M Taram, P Vepakomma, A Singh, R Raskar, ... | 138 | 2020 |
| Split Learning for collaborative deep learning in healthcare MG Poirot, P Vepakomma, K Chang, J Kalpathy-Cramer, R Gupta, ... | 138 | 2019 |
| Apps gone rogue: Maintaining personal privacy in an epidemic R Raskar, I Schunemann, R Barbar, K Vilcans, J Gray, P Vepakomma, ... arXiv preprint arXiv:2003.08567, 2020 | 136 | 2020 |
| No peek: A survey of private distributed deep learning P Vepakomma, T Swedish, R Raskar, O Gupta, A and Dubey arXiv preprint arXiv:1812.03288 8, 2018 | 120 | 2018 |
| Reducing Leakage In Distributed Deep Learning For Sensitive Health Data P Vepakomma, O Gupta, D Abhimanyu, R Raskar ICLR AI for Social Good, 2019 | 97 | 2019 |
| Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic A Berke, M Bakker, P Vepakomma, R Raskar, K Larson, AS Pentland | 79* | 2020 |
| Splitnn-driven vertical partitioning I Ceballos, V Sharma, E Mugica, A Singh, A Roman, P Vepakomma, ... arXiv preprint arXiv:2008.04137, 2020 | 60 | 2020 |
| Tristan Swedish, and Ramesh Raskar. 2018. Split learning for health: Distributed deep learning without sharing raw patient data P Vepakomma, O Gupta arXiv preprint arXiv:1812.00564, 2018 | 57 | 2018 |
| A Review of Homomorphic Encryption Libraries for Secure Computation SS Sathya, P Vepakomma, R Raskar, R Ramachandra, S Bhattacharya | 57 | 2018 |
| Supervised Dimensionality Reduction via Distance Correlation Maximization P Vepakomma, C Tonde, A Elgammal Electronic Journal of Statistics (Journal) 12 (1), 960-984, 2018 | 48 | 2018 |
| DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks A Singh, A Chopra, V Sharma, E Garza, E Zhang, P Vepakomma, ... IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021 | 33 | 2021 |
| LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning S Oh, J Park, P Vepakomma, S Baek, R Raskar, ... The Web Conference, (WWW 2022), 2022 | 30 | 2022 |
| Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning S Pal, M Uniyal, J Park, P Vepakomma, R Raskar, M Bennis, M Jeon, ... arXiv preprint arXiv:2112.05929, 2022 | 18 | 2022 |
| Adasplit: Adaptive trade-offs for resource-constrained distributed deep learning A Chopra, SK Sahu, A Singh, A Java, P Vepakomma, V Sharma, ... arXiv preprint arXiv:2112.01637, 2021 | 17 | 2021 |
| A Fast Algorithm for Manifold Learning by Posing it as a Symmetric Diagonally Dominant Linear System P Vepakomma, A Elgammal Applied and Computational Harmonic Analysis, 2016 | 15 | 2016 |
Ramesh RaskarAssociate Professor, MIT Media Lab在 mit.edu 的电子邮件经过验证
